Inter-personal diversity and temporal dynamics of dental, tongue, and salivary microbiota in the healthy oral cavity
Bibliographic record
Abstract
Abstract Oral microbes form a complex and dynamic biofilm community, which is subjected to daily host and environmental challenges. Dysbiosis of the oral biofilm is correlated with local and distal infections and postulating a baseline for the healthy core oral microbiota provides an opportunity to examine such shifts during the onset and recurrence of disease. Here we quantified the daily, weekly, and monthly variability of the oral microbiome by sequencing the largest oral microbiota time-series to date, covering multiple oral sites in ten healthy individuals. Temporal dynamics of salivary, dental, and tongue consortia were examined by high-throughput 16S rRNA gene sequencing over 90 days, with four individuals sampled additionally 1 year later. Distinct communities were observed between dental, tongue, and salivary samples, with high levels of similarity observed between the tongue and salivary communities. Twenty-six core OTUs that classified within Streptococcus, Fusobacterium, Haemophilus, Neisseria, Prevotella , and Rothia genera were present in ≥95% samples and accounted for ~65% of the total sequence data. Phylogenetic diversity varied from person to person, but remained relatively stable within individuals over time compared to inter-individual variation. In contrast, the composition of rare microorganisms was highly variable over time, within most individuals. Using machine learning, an individual's oral microbial assemblage could be correctly assigned to them with 88–97% accuracy, depending on the sample site; 83% of samples taken a year after initial sampling could be confidently traced back to the source subject.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".